Blind signal separation (BSS) techniques involve recovering source signals from a composite signal, wherein the composite signal includes a mixture of the source signals. The separation is “blind” because it is often performed with limited information about the signals, the sources of the signals, and the effects that the propagation channel has on the signals.
Blind source separation is particularly applicable to cellular and personal wireless communications devices, where many frequency bands have become cluttered with numerous radio frequency emitters, often co-existing in the same spectrum. The problem of co-channel emitters is expected to only worsen in years to come with the development of low power, unlicensed wireless technologies such as Bluetooth and other personal area networks.
Three commonly used blind signal separation techniques are principal component analysis (PCA), independent component analysis (ICA) and singular value decomposition (SVD). PCA involves first and second moment statistics of the source signals, and is used when the signal-to-noise ratios of the source signals are high. Otherwise, ICA is used which involves the third and fourth moment statistics of the source signals. ICA is often preceded by a whitening process which improves the condition number of the mixing matrix. PCA is often the choice for such preprocessing. As an alternative, SVD may be used to separate a source signal from the mixture of source signals based upon their eigenvalues.
While these three are the most often encountered processing means, it should be recognized that there are many variations, combinations, and related means that are all within the scope of the basic processing approach. This includes using processing which is semi-blind, meaning that some knowledge of the signals is exploited to enhance the overall processing.
Regardless of the blind signal separation technique that is applied, an antenna array comprising a plurality of antenna elements is typically used to receive different mixtures of the source signals from the various signal sources. Each antenna element outputs a mixture of the source signals, which is ideally a unique sum of the source signals. The unique sums of signals are used to populate a mixing matrix. The appropriate signal separation technique is then applied to the mixing matrix for separating desired source signals from the mixture of source signals.
In general, the rank of the mixing matrix determines the number of the most significant signals that will be separated, while the rest of the signals are treated as noise. This value therefore needs to be at a minimum inclusive of the signals to be decoded. A possibly higher minimum may be necessary to decrease the noise component so that the signal to noise ratio allows an acceptable decoding error rate.
Typically, the size of the mixing matrix remains fixed based upon a function of the number of antenna elements receiving linearly independent summations of the source signals. When separating and decoding a desired signal from the different summations of the source signals, the size of the mixing matrix may be adequate, over-determined or under-determined. Over-determined means that the mixing matrix is larger than what is necessary for adequately separating and decoding the desired signal. Under-determined means that the mixing matrix is less than what is necessary for adequately separating and decoding the desired signal. Over/under-determination of the mixing matrix has a negative effect on optimizing processing, power drain and communication link attributes for the communications device.